A general multi-agent framework has been proposed in [1]} for mixed cooperative and competitive multi-agent environments. Moving from the common assumption in multi-agent studies, which assumes observations and policies of all agents are available at every agent, this work proposed to learn the approximation of other agents' policies via the maximization of the log probability of the agent. In order to maintain the robustness of the of proposed policy, the agents employ policy ensembles that are trained for a wide range of policies. Nevertheless, the developed framework allows to evaluate the impact of the communication among the agents on the learning policy by limiting the observed information from other agents.
